A high-order discontinuous Galerkin solver for the incompressible RANS equations coupled to the k−ϵ turbulence model

Research output: Contribution to journalArticleScientificpeer-review

1 Citation (Scopus)
4 Downloads (Pure)

Abstract

Accurate methods to solve the Reynolds-Averaged Navier-Stokes (RANS) equations coupled to turbulence models are still of great interest, as this is often the only computationally feasible approach to simulate complex turbulent flows in large engineering applications. In this work, we present a novel discontinuous Galerkin (DG) solver for the RANS equations coupled to the k−ϵ model (in logarithmic form, to ensure positivity of the turbulence quantities). We investigate the possibility of modeling walls with a wall function approach in combination with DG. The solver features an algebraic pressure correction scheme to solve the coupled RANS system, implicit backward differentiation formulae for time discretization, and adopts the Symmetric Interior Penalty method and the Lax-Friedrichs flux to discretize diffusive and convective terms respectively. We pay special attention to the choice of polynomial order for any transported scalar quantity and show it has to be the same as the pressure order to avoid numerical instability. A manufactured solution is used to verify that the solution converges with the expected order of accuracy in space and time. We then simulate a stationary flow over a backward-facing step and a Von Kármán vortex street in the wake of a square cylinder to validate our approach.

Original languageEnglish
Article number104710
Number of pages15
JournalComputers and Fluids
Volume212
DOIs
Publication statusPublished - 2020

Keywords

  • Discontinuous Galerkin FEM
  • Incompressible RANS
  • k−ϵ turbulence model
  • Pressure correction
  • Symmetric interior penalty method
  • Wall function

Fingerprint Dive into the research topics of 'A high-order discontinuous Galerkin solver for the incompressible RANS equations coupled to the k−ϵ turbulence model'. Together they form a unique fingerprint.

Cite this